docs: clarify save vs save_weights for TFQ models

docs/concepts.md

@@ -35,7 +35,7 @@ and machine learning.
 system. This can be data generated by a quantum computer, like the samples
 gathered from the
 <a href="https://www.nature.com/articles/s41586-019-1666-5" class="external">Sycamore processor</a>
-for Google’s demonstration of quantum supremacy. Quantum data exhibits
+for Google's demonstration of quantum supremacy. Quantum data exhibits
 superposition and entanglement, leading to joint probability distributions that
 could require an exponential amount of classical computational resources to
 represent or store. The quantum supremacy experiment showed it is possible to
@@ -85,6 +85,14 @@ A *quantum neural network* (QNN) is used to describe a parameterized quantum
 computational model that is best executed on a quantum computer. This term is
 often interchangeable with *parameterized quantum circuit* (PQC).
 
+#### Saving models
+
+Note: `model.save()` does not work for TensorFlow Quantum models containing
+custom quantum layers, because full model serialization requires the layer
+to be reconstructable without the original Python code. Instead, use
+`model.save_weights()` to save the trained parameters, then rebuild the model
+architecture in Python and restore them with `model.load_weights()`.
+
 
 ## Research
 
@@ -109,9 +117,9 @@ with particular interest in:
    quality quantum gates.
 2. *Model quantum data with quantum circuits.* Classically modeling quantum data
    is possible if you have an exact description of the datasource—but sometimes
-   this isn’t possible. To solve this problem, you can try modeling on the
+   this isn't possible. To solve this problem, you can try modeling on the
    quantum computer itself and measure/observe the important statistics.
-   <a href="https://www.nature.com/articles/s41567-019-0648-8" class="external">Quantum convolutional neural networks</a>
+   <a href="https://arxiv.org/abs/1711.07500" class="external">Quantum convolutional neural networks</a>
    shows a quantum circuit designed with a structure analogous to a
    convolutional neural network (CNN) to detect different topological phases of
    matter. The quantum computer holds the data and the model. The classical

scripts/ci_validate_tutorials.sh

@@ -32,6 +32,9 @@ pip install gymnasium[classic-control]==1.2.3
 pip install seaborn==0.12.0
 # tf_docs pip package needed for noise tutorial.
 pip install -q git+https://github.com/tensorflow/docs
+# pydot and graphviz needed for tf.keras.utils.plot_model in tutorials
+pip install pydot
+sudo apt-get install -y graphviz
 # Leave the quantum directory, otherwise errors may occur
 cd ..